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Economic impact analysis using Big Data

 

Big Data improves both the forecasting of economic developments and the analysis of their impact. Whereas dramatic advances in forecasting have been made in the past, use of this data for measuring impact is still in its infancy. The aim of this project is therefore to refine impact measurement methods and apply them to a selected sample of research questions.

Portrait / project description (ongoing research project)

In the first part of the project, we will combine causal analysis methods from microeconometrics with the statistical methods of machine learning. We will first examine the properties of the resulting new statistical processes using simulation methods. We will then investigate the practical feasibility of the methods – and optimise them – in three areas of application: 1) impact analysis of a labour market economic policy programme, 2) pricing in online used-car markets and 3) exposure of the possible discrimination of professional football players.

Background

In recent years, microeconometric research has made great advances in the development of methodological tools for answering causal questions. These methods – e.g. for the assessment of economic policy measures – have been successfully employed. Unfortunately, these tools are largely unsuitable for analysing complex data volumes. Can methods be enhanced in such a way as to significantly advance the use of Big Data for impact measurement?

Aim

The goal of the present project is to combine the microeconometric methods of causal analysis (impact measurement) and the statistical forecasting models of machine learning to be able to use large-volume data sets to substantially improve the impact analysis of decisions taken by economic policymakers and private sector actors.

Relevance/application

A successful outcome to the project could lead to much more reliable statements of the impact of individual measures and decisions in numerous economic contexts. While this would facilitate more efficient (since evidence-based) economic policymaking for the public sector, companies in the private sector would also benefit from improved decision-making tools.

Original title

Causal analysis with Big Data

Project leader

Prof. Michael Lechner, Schweizerisches Institut für Empirische Wirtschaftsforschung, Universität St. Gallen

 

 

Further information on this content

 Contact

Prof. Michael Lechner Schweizerisches Institut für Empirische Wirtschaftsforschung Varnbüelstrasse 14 9000 St. Gallen michael.lechner@unisg.ch

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